Semantic Segmentation of Outcrop Images using Deep Learning Networks Toward Realization of Carbon Capture and Storage

• Main Authors: Sato, Kodai, Madokoro, Hirokazu, Nagayoshi, Takeshi, Chiyonobu, Shun, Martizzi, Paolo, Nix, Stephanie, Woo, Hanwool, Saito, Takashi K., Sato, Kazuhito
• Format: Conference Proceeding
• Language: English
• Subjects: Atmospheric measurements; Atmospheric modeling; backbones; carbon capture and storage; Deep learning; Geology; Image segmentation; Outcrop images; Production; semantic segmentation; Semantics
• ISSN: 2642-3901
• DOI: 10.23919/ICCAS52745.2021.9649777

This study was conducted to classify outcrop images using semantic segmentation methods based on deep learning algorithms. Carbon capture and storage (CCS) is an epoch-making approach to reduce greenhouse gases in the atmosphere. This study specifically examines outcrops because geological layer measurements can lead to production of a highly accurate geological model for feasible CCS inspections. Using a digital monocular RGB camera, we obtained 13 outcrop images annotated with four classes along with strata. Subsequently, we compared segmentation accuracies with changing input image sizes of three types and semantic segmentation methods of four backbones: SegNet, U-Net, ResNet-18, and Xception-65. The ResNet-18 and Xception-65 backbones were implemented using DeepLabv3+. Experimentally obtained results demonstrated that data expansion with random sampling improved the accuracy. Regarding evaluation metrics, global accuracy and local accuracy are higher than the mean intersection over union (mIoU) for our outcrop image dataset with unequal numbers of pixels in the respective classes. These experimentally obtained results revealed that resizing for input images is unnecessary for our method.